4 citations · 11 across the 12 of their papers we have counts for
12 papers
HyperDFlash: Hyper-Connection-Aligned Block Speculative Decoding with Gated Residual Reduction
Luxi Lin, Shuang Peng, Rui Ma +7
We present HyperDFlash, a block-parallel speculative decoding framework tailored to DeepSeek-V4's Hyper-Connections (HC). Despite the strong performance of DeepSeek-V4's native Mul…
TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration
Haowei Zhu, Tingxuan Huang, Xing Wang +7
Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (T…
S2O: Early Stopping for Sparse Attention via Online Permutation
Yu Zhang, Songwei Liu, Chenqian Yan +4
Attention scales quadratically with sequence length, fundamentally limiting long-context inference. Existing block-granularity sparsification can reduce latency, but coarse blocks…
ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion
Xurui Peng, Chenqian Yan, Hong Liu +6
Diffusion models suffer from substantial computational overhead due to their inherently iterative inference process. While feature caching offers a promising acceleration strategy…
Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion
Songwei Liu, Chao Zeng, Chenqian Yan +4
Diffusion models have transformed image synthesis by establishing unprecedented quality and creativity benchmarks. Nevertheless, their large-scale deployment faces challenges due t…
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference
Chao Zeng, Songwei Liu, Shu Yang +3
Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a n…
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Chao Zeng, Songwei Liu, Yusheng Xie +6
Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational dema…
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models
Chenqian Yan, Songwei Liu, Hongjian Liu +5
Stable Diffusion Models (SDMs) have shown remarkable proficiency in image synthesis. However, their broad application is impeded by their large model sizes and intensive computatio…
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Songwei Liu, Chao Zeng, Lianqiang Li +4
The demand for deploying large language models(LLMs) on mobile devices continues to increase, driven by escalating data security concerns and cloud costs. However, network bandwidt…
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity
Haitao Xu, Songwei Liu, Yuyang Xu +7
To address the challenge of increasing network size, researchers have developed sparse models through network pruning. However, maintaining model accuracy while achieving significa…
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Yong Liu, Hang Dong, Boyang Liang +6
Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increa…
Residual Local Feature Network for Efficient Super-Resolution
Fangyuan Kong, Mingxi Li, Songwei Liu +5
Deep learning based approaches has achieved great performance in single image super-resolution (SISR). However, recent advances in efficient super-resolution focus on reducing the…